class_docstr: Deconvolution upsample module in decoder for UNet (2X upsample).
  
  This module uses deconvolution to upsample feature map in the decoder
  of UNet.
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      with_cp (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed. Default: False.
      norm_cfg (dict | None): Config dict for normalization layer.
          Default: dict(type='BN').
      act_cfg (dict | None): Config dict for activation layer in ConvModule.
          Default: dict(type='ReLU').
      kernel_size (int): Kernel size of the convolutional layer. Default: 4.
docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, x):
      """Forward function."""
  
      if self.with_cp and x.requires_grad:
          out = cp.checkpoint(self.deconv_upsamping, x)
      else:
          out = self.deconv_upsamping(x)
      return out
  
imports: import torch
  import torch.nn
  import torch.utils.checkpoint
  from mmcv.cnn import ConvModule, build_norm_layer
  from mmengine.model import BaseModule
  from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
  from mmocr.registry import MODELS
label_desc: Use image node_id 2 for calling the DeconvModule obj's underlying member method code with example usage: obj.forward(x) and returns: out